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1
- ---
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- tags:
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- - setfit
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- - sentence-transformers
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- - text-classification
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- - generated_from_setfit_trainer
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- widget:
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- - text: Mujhe apne galtiyon ka ehsaas hai aur main unke liye maafi chahta hoon.
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- - text: Mujhe yeh step samajhne mein dikkat ho rahi hai, kya aap madad kar sakte hain?
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- - text: Mujhe abhi tak kuch update kyun nahi mila, yeh bahut frustrating hai.
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- - text: Is app ka loading time mujhe thoda zyada lagta hai.
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- - text: Kya aap mujhe is event ki timing bata sakte hain?
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- metrics:
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- - accuracy
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- pipeline_tag: text-classification
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- library_name: setfit
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- inference: true
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- base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
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- model-index:
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- - name: SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
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- results:
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- - task:
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- type: text-classification
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- name: Text Classification
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- dataset:
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- name: Unknown
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- type: unknown
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- split: test
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- metrics:
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- - type: accuracy
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- value: 0.32
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- name: Accuracy
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- ---
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-
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- # SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
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-
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- This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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-
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- The model has been trained using an efficient few-shot learning technique that involves:
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-
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- 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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- 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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-
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- ## Model Details
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-
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- ### Model Description
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- - **Model Type:** SetFit
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- - **Sentence Transformer body:** [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)
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- - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- - **Maximum Sequence Length:** 512 tokens
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- - **Number of Classes:** 19 classes
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- <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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- <!-- - **Language:** Unknown -->
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- <!-- - **License:** Unknown -->
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-
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- ### Model Sources
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-
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- - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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- - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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- - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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-
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- ### Model Labels
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- | Label | Examples |
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- |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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- | 4 | <ul><li>'Yeh rahin wo steps jisse aap apni payment kar sakte hain.'</li><li>'Kya aap mujhe yeh batane ka tarika thoda aasan kar sakte hain?'</li><li>'Is option ke madhyam se aap apni queries kaise solve kar sakte hain, jaan lijiye.'</li></ul> |
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- | 16 | <ul><li>'Aapke feedback ko humne dhyan mein rakha hai.'</li><li>'Yeh galti humare systems ki wajah se hui hai.'</li><li>'Mujhe is samasya ko suljhane mein zyada samay lena nahi chahiye tha.'</li></ul> |
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- | 8 | <ul><li>'Main aapko pareshan karne ke liye maafi chahta hoon.'</li><li>'Humein is samasya ke liye maafi chahiye.'</li><li>'Mere kaam se agar aapko takleef hui ho, toh mujhe maaf kar dijiye.'</li></ul> |
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- | 13 | <ul><li>'Mujhe yeh clarify karne ki zarurat hai ki agla step kya hai?'</li><li>'Mujhe pata karna hai ki maine jo complaint ki thi uska kya hua.'</li><li>'Mujhe bataye ki pehle kitne payments honge iss plan ke liye.'</li></ul> |
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- | 15 | <ul><li>'Yeh features sahi hai, lekin kuch aur additional functionalities honi chahiye.'</li><li>'Product ke size ki jankari hamesha saaf honi chahiye.'</li><li>'Main chahunga ki online form aur simple ho.'</li></ul> |
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- | 12 | <ul><li>'Mujhe product ke sath kuch samasya hai.'</li><li>'Mera phone charging nahi ho raha.'</li><li>'Mujhe courier service mein dikkat hai, report karna hai.'</li></ul> |
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- | 11 | <ul><li>'Mujhe samajh nahi aa raha, is offer mein koi chhupi shartein toh nahi hai?'</li><li>'Kis tarah se main feedback de sakta hoon?'</li><li>'Kya koi referral program hai jo mujhe join karna chahiye?'</li></ul> |
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- | 2 | <ul><li>'Item ke sath saathi accessories nahi mil rahe hain.'</li><li>'Aap logon ne jo samay liya, wo bilkul zyada tha.'</li><li>'Meri order delivery mein bahut der ho gayi hai.'</li></ul> |
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- | 18 | <ul><li>'Mujhe yeh bilkul pasand nahi hai ki meri baat ignore ki gayi.'</li><li>'Kam ke liye mera dosto ka support bahut sukhdayak hai.'</li><li>'Aaj ka din kaafi udaas beete raha hai.'</li></ul> |
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- | 14 | <ul><li>'Kya main kal ki delivery ko agle hafte reschedule kar sakta/sakti hoon?'</li><li>'Mujhe refund ke liye kya documents chahiye?'</li><li>'Kya main appointment ko dobara set kar sakta/sakti hoon?'</li></ul> |
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- | 7 | <ul><li>'Main aapko dhanyavad dena chahta hoon, aapne meri madad ki.'</li><li>'Aapne jo kiya, uske liye aapko sabse pehle prashansha milni chahiye.'</li><li>'Aapka samay dene ke liye abhaar.'</li></ul> |
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- | 3 | <ul><li>'Mujhe kisi event ke tickets ka status check karna hai.'</li><li>'Kya aap mujhe customer support number de sakte hain?'</li><li>'Main apne account ka balance kaise check kar sakta/sakti hoon?'</li></ul> |
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- | 5 | <ul><li>'Alvida, tumhara din acha rahe!'</li><li>'Hello! Aaj aap kaise hain?'</li><li>'Swagat hai! Kya main aapki kuch madad kar sakta hoon?'</li></ul> |
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- | 0 | <ul><li>'Mujhe kuch samajh nahi aa raha hai, kya mujhe thoda aur samjha sakte hain?'</li><li>'Agar main aisa karoon, to kya kuch badal jaayega? Main sure nahi hoon.'</li><li>'Yeh product ki warranty ki details clear nahi hain.'</li></ul> |
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- | 6 | <ul><li>'Chalo, alvida bolte hain!'</li><li>'Phir se baat karte hain!'</li><li>'Adieu, aapka din shubh ho!'</li></ul> |
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- | 17 | <ul><li>'Mere account mein login karne mein dikkat aa rahi hai, madad karein.'</li><li>'Mujhe apne account mein login karne mein madad chahiye.'</li><li>'Kya aap mujhe terms and conditions ke details de sakte hain?'</li></ul> |
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- | 10 | <ul><li>'Main aapki baat se sehmat hoon.'</li><li>'Mujhe yeh batayein ki meri booking sahi hai na?'</li></ul> |
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- | 9 | <ul><li>'Kya aap mujhe yeh concept aur clear kar sakte hain?'</li><li>'Mujhe yeh samajhne mein dikkat ho rahi hai, kya aap vyakhya de sakte hain?'</li></ul> |
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- | 1 | <ul><li>'Aaj dosto ke sath waqt bitana bahut acha laga.'</li><li>'Aaj baarish me bheegna bahut refreshing tha, mujhe yeh moment pasand aaya.'</li><li>'Aapka support bahut madadgar raha.'</li></ul> |
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-
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- ## Evaluation
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-
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- ### Metrics
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- | Label | Accuracy |
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- |:--------|:---------|
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- | **all** | 0.32 |
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-
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- ## Uses
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-
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- ### Direct Use for Inference
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-
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- First install the SetFit library:
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-
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- ```bash
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- pip install setfit
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- ```
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-
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- Then you can load this model and run inference.
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-
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- ```python
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- from setfit import SetFitModel
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-
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- # Download from the 🤗 Hub
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- model = SetFitModel.from_pretrained("setfit_model_id")
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- # Run inference
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- preds = model("Kya aap mujhe is event ki timing bata sakte hain?")
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- ```
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-
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- <!--
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- ### Downstream Use
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-
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- *List how someone could finetune this model on their own dataset.*
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- -->
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-
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- <!--
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- ### Out-of-Scope Use
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-
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- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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- -->
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-
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- <!--
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- ## Bias, Risks and Limitations
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-
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- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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- -->
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-
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- <!--
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- ### Recommendations
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-
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- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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- -->
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-
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- ## Training Details
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-
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- ### Training Set Metrics
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- | Training set | Min | Median | Max |
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- |:-------------|:----|:-------|:----|
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- | Word count | 3 | 9.76 | 15 |
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-
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- | Label | Training Sample Count |
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- |:------|:----------------------|
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- | 0 | 6 |
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- | 1 | 3 |
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- | 2 | 3 |
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- | 3 | 5 |
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- | 4 | 7 |
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- | 5 | 3 |
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- | 6 | 6 |
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- | 7 | 8 |
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- | 8 | 6 |
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- | 9 | 2 |
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- | 10 | 2 |
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- | 11 | 5 |
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- | 12 | 6 |
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- | 13 | 5 |
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- | 14 | 9 |
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- | 15 | 9 |
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- | 16 | 9 |
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- | 17 | 3 |
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- | 18 | 3 |
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-
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- ### Training Hyperparameters
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- - batch_size: (16, 2)
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- - num_epochs: (1, 16)
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- - max_steps: -1
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- - sampling_strategy: oversampling
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- - body_learning_rate: (2e-05, 1e-05)
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- - head_learning_rate: 0.01
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- - loss: CosineSimilarityLoss
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- - distance_metric: cosine_distance
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- - margin: 0.25
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- - end_to_end: False
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- - use_amp: False
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- - warmup_proportion: 0.1
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- - l2_weight: 0.01
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- - seed: 42
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- - eval_max_steps: -1
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- - load_best_model_at_end: False
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-
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- ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:------:|:----:|:-------------:|:---------------:|
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- | 0.0017 | 1 | 0.2335 | - |
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- | 0.0853 | 50 | 0.2514 | - |
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- | 0.1706 | 100 | 0.1619 | - |
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- | 0.2560 | 150 | 0.1124 | - |
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- | 0.3413 | 200 | 0.078 | - |
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- | 0.4266 | 250 | 0.0623 | - |
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- | 0.5119 | 300 | 0.0576 | - |
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- | 0.5973 | 350 | 0.0421 | - |
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- | 0.6826 | 400 | 0.0391 | - |
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- | 0.7679 | 450 | 0.0386 | - |
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- | 0.8532 | 500 | 0.0302 | - |
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- | 0.9386 | 550 | 0.0245 | - |
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-
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- ### Framework Versions
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- - Python: 3.10.16
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- - SetFit: 1.1.1
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- - Sentence Transformers: 3.3.1
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- - Transformers: 4.46.3
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- - PyTorch: 2.5.1+cpu
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- - Datasets: 3.2.0
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- - Tokenizers: 0.20.3
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-
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- ## Citation
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-
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- ### BibTeX
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- ```bibtex
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- @article{https://doi.org/10.48550/arxiv.2209.11055,
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- doi = {10.48550/ARXIV.2209.11055},
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- url = {https://arxiv.org/abs/2209.11055},
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- author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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- keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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- title = {Efficient Few-Shot Learning Without Prompts},
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- publisher = {arXiv},
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- year = {2022},
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- copyright = {Creative Commons Attribution 4.0 International}
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- }
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- ```
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-
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- <!--
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- ## Glossary
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-
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- *Clearly define terms in order to be accessible across audiences.*
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- -->
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-
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- <!--
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- ## Model Card Authors
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-
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- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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- -->
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-
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- <!--
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- ## Model Card Contact
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-
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- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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  -->
 
1
+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: Mujhe apne galtiyon ka ehsaas hai aur main unke liye maafi chahta hoon.
9
+ - text: Mujhe yeh step samajhne mein dikkat ho rahi hai, kya aap madad kar sakte hain?
10
+ - text: Mujhe abhi tak kuch update kyun nahi mila, yeh bahut frustrating hai.
11
+ - text: Is app ka loading time mujhe thoda zyada lagta hai.
12
+ - text: Kya aap mujhe is event ki timing bata sakte hain?
13
+ metrics:
14
+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
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+ model-index:
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+ - name: SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.32
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+ name: Accuracy
33
+ ---
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+
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+ # SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
38
+
39
+ The model has been trained using an efficient few-shot learning technique that involves:
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+
41
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
42
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
50
+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 19 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
59
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
63
+ | Label | Examples |
64
+ |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
65
+ | 4 | <ul><li>'Yeh rahin wo steps jisse aap apni payment kar sakte hain.'</li><li>'Kya aap mujhe yeh batane ka tarika thoda aasan kar sakte hain?'</li><li>'Is option ke madhyam se aap apni queries kaise solve kar sakte hain, jaan lijiye.'</li></ul> |
66
+ | 16 | <ul><li>'Aapke feedback ko humne dhyan mein rakha hai.'</li><li>'Yeh galti humare systems ki wajah se hui hai.'</li><li>'Mujhe is samasya ko suljhane mein zyada samay lena nahi chahiye tha.'</li></ul> |
67
+ | 8 | <ul><li>'Main aapko pareshan karne ke liye maafi chahta hoon.'</li><li>'Humein is samasya ke liye maafi chahiye.'</li><li>'Mere kaam se agar aapko takleef hui ho, toh mujhe maaf kar dijiye.'</li></ul> |
68
+ | 13 | <ul><li>'Mujhe yeh clarify karne ki zarurat hai ki agla step kya hai?'</li><li>'Mujhe pata karna hai ki maine jo complaint ki thi uska kya hua.'</li><li>'Mujhe bataye ki pehle kitne payments honge iss plan ke liye.'</li></ul> |
69
+ | 15 | <ul><li>'Yeh features sahi hai, lekin kuch aur additional functionalities honi chahiye.'</li><li>'Product ke size ki jankari hamesha saaf honi chahiye.'</li><li>'Main chahunga ki online form aur simple ho.'</li></ul> |
70
+ | 12 | <ul><li>'Mujhe product ke sath kuch samasya hai.'</li><li>'Mera phone charging nahi ho raha.'</li><li>'Mujhe courier service mein dikkat hai, report karna hai.'</li></ul> |
71
+ | 11 | <ul><li>'Mujhe samajh nahi aa raha, is offer mein koi chhupi shartein toh nahi hai?'</li><li>'Kis tarah se main feedback de sakta hoon?'</li><li>'Kya koi referral program hai jo mujhe join karna chahiye?'</li></ul> |
72
+ | 2 | <ul><li>'Item ke sath saathi accessories nahi mil rahe hain.'</li><li>'Aap logon ne jo samay liya, wo bilkul zyada tha.'</li><li>'Meri order delivery mein bahut der ho gayi hai.'</li></ul> |
73
+ | 18 | <ul><li>'Mujhe yeh bilkul pasand nahi hai ki meri baat ignore ki gayi.'</li><li>'Kam ke liye mera dosto ka support bahut sukhdayak hai.'</li><li>'Aaj ka din kaafi udaas beete raha hai.'</li></ul> |
74
+ | 14 | <ul><li>'Kya main kal ki delivery ko agle hafte reschedule kar sakta/sakti hoon?'</li><li>'Mujhe refund ke liye kya documents chahiye?'</li><li>'Kya main appointment ko dobara set kar sakta/sakti hoon?'</li></ul> |
75
+ | 7 | <ul><li>'Main aapko dhanyavad dena chahta hoon, aapne meri madad ki.'</li><li>'Aapne jo kiya, uske liye aapko sabse pehle prashansha milni chahiye.'</li><li>'Aapka samay dene ke liye abhaar.'</li></ul> |
76
+ | 3 | <ul><li>'Mujhe kisi event ke tickets ka status check karna hai.'</li><li>'Kya aap mujhe customer support number de sakte hain?'</li><li>'Main apne account ka balance kaise check kar sakta/sakti hoon?'</li></ul> |
77
+ | 5 | <ul><li>'Alvida, tumhara din acha rahe!'</li><li>'Hello! Aaj aap kaise hain?'</li><li>'Swagat hai! Kya main aapki kuch madad kar sakta hoon?'</li></ul> |
78
+ | 0 | <ul><li>'Mujhe kuch samajh nahi aa raha hai, kya mujhe thoda aur samjha sakte hain?'</li><li>'Agar main aisa karoon, to kya kuch badal jaayega? Main sure nahi hoon.'</li><li>'Yeh product ki warranty ki details clear nahi hain.'</li></ul> |
79
+ | 6 | <ul><li>'Chalo, alvida bolte hain!'</li><li>'Phir se baat karte hain!'</li><li>'Adieu, aapka din shubh ho!'</li></ul> |
80
+ | 17 | <ul><li>'Mere account mein login karne mein dikkat aa rahi hai, madad karein.'</li><li>'Mujhe apne account mein login karne mein madad chahiye.'</li><li>'Kya aap mujhe terms and conditions ke details de sakte hain?'</li></ul> |
81
+ | 10 | <ul><li>'Main aapki baat se sehmat hoon.'</li><li>'Mujhe yeh batayein ki meri booking sahi hai na?'</li></ul> |
82
+ | 9 | <ul><li>'Kya aap mujhe yeh concept aur clear kar sakte hain?'</li><li>'Mujhe yeh samajhne mein dikkat ho rahi hai, kya aap vyakhya de sakte hain?'</li></ul> |
83
+ | 1 | <ul><li>'Aaj dosto ke sath waqt bitana bahut acha laga.'</li><li>'Aaj baarish me bheegna bahut refreshing tha, mujhe yeh moment pasand aaya.'</li><li>'Aapka support bahut madadgar raha.'</li></ul> |
84
+
85
+ ## Evaluation
86
+
87
+ ### Metrics
88
+ | Label | Accuracy |
89
+ |:--------|:---------|
90
+ | **all** | 0.32 |
91
+
92
+ ## Uses
93
+
94
+ ### Direct Use for Inference
95
+
96
+ First install the SetFit library:
97
+
98
+ ```bash
99
+ pip install setfit
100
+ ```
101
+
102
+ Then you can load this model and run inference.
103
+
104
+ ```python
105
+ from setfit import SetFitModel
106
+
107
+ # Download from the 🤗 Hub
108
+ model = SetFitModel.from_pretrained("rbojja/FT-mDeBERTa-v3-base-mnli-xnli")
109
+ # Run inference
110
+ preds = model("Kya aap mujhe is event ki timing bata sakte hain?")
111
+ ```
112
+
113
+ <!--
114
+ ### Downstream Use
115
+
116
+ *List how someone could finetune this model on their own dataset.*
117
+ -->
118
+
119
+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 3 | 9.76 | 15 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 6 |
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+ | 1 | 3 |
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+ | 2 | 3 |
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+ | 3 | 5 |
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+ | 4 | 7 |
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+ | 5 | 3 |
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+ | 6 | 6 |
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+ | 7 | 8 |
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+ | 8 | 6 |
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+ | 9 | 2 |
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+ | 10 | 2 |
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+ | 11 | 5 |
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+ | 12 | 6 |
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+ | 13 | 5 |
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+ | 14 | 9 |
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+ | 15 | 9 |
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+ | 16 | 9 |
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+ | 17 | 3 |
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+ | 18 | 3 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (1, 16)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0017 | 1 | 0.2335 | - |
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+ | 0.0853 | 50 | 0.2514 | - |
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+ | 0.1706 | 100 | 0.1619 | - |
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+ | 0.2560 | 150 | 0.1124 | - |
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+ | 0.3413 | 200 | 0.078 | - |
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+ | 0.4266 | 250 | 0.0623 | - |
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+ | 0.5119 | 300 | 0.0576 | - |
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+ | 0.5973 | 350 | 0.0421 | - |
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+ | 0.6826 | 400 | 0.0391 | - |
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+ | 0.7679 | 450 | 0.0386 | - |
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+ | 0.8532 | 500 | 0.0302 | - |
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+ | 0.9386 | 550 | 0.0245 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.16
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+ - SetFit: 1.1.1
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+ - Sentence Transformers: 3.3.1
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+ - Transformers: 4.46.3
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+ - PyTorch: 2.5.1+cpu
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.20.3
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+
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+ ## Citation
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+
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+ ### BibTeX
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+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
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+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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  -->